AWS publishes guide for building AI agents with Amazon Bedrock and Spring AI

Amazon Web Services published a technical walkthrough on building autonomous AI agents with Amazon Bedrock and Spring AI. The guide is aimed at Java teams that want to move AI agents from prototype to production.

What the walkthrough covers

The post describes how to build autonomous AI agents in Java using Spring AI and Amazon Bedrock with the spring-ai-agentcore library. It presents two systems:

• A single-agent travel assistant that demonstrates tool calling, streaming responses, conversation memory, and resilience patterns.
• A multi-agent intelligent document processing system with five specialist agents (Lawyer, Mortgage, Insurance, Tax, and General), a rules engine, and alerting.

Agent capabilities

The guide defines an agent as a system that unites three capabilities:

• Planning: breaking a goal into steps, prioritizing them, and revising the plan as new information arrives.
• Tool use: selecting and invoking external functions such as APIs, databases, and code interpreters.
• Memory: carrying context across turns and across sessions.

Amazon Bedrock AgentCore

Amazon Bedrock AgentCore is described as a platform to build, connect, and optimize agents at scale, with any framework or model. It is framework-agnostic and model-agnostic. The platform provides managed runtime, memory, browser automation, code execution, and identity management.

Implementation details

The travel assistant runs as a Spring Boot application with two entry points: POST /invocations and GET /ping, which are required by the AgentCore runtime contract. Spring AI orchestrates calls to Amazon Bedrock through the Converse API while tool invocation runs locally inside the same process. Server-Sent Events stream tokens to the client as they are generated.

The IDP multi-agent system routes documents through specialist agents built on a shared template. A ClassifierAgent reads each document and forwards it to the appropriate specialist. The specialist extracts structured JSON with confidence scores using the multimodal Converse API of Amazon Bedrock. A rules engine evaluates Spring Expression Language expressions from application.yml against the extraction. ALERT or BLOCK severity rules dispatch through an AlertService. Low-confidence documents go to human review.

Why it matters

The guide addresses scalability, governance, security, and operational concerns that agent frameworks do not solve by default. It gives Java teams a production-oriented path for building autonomous AI agents using existing JVM tooling, CI/CD pipelines, and operational patterns.

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Sources

• aws.amazon.com: Build autonomous AI agents with Amazon Bedrock and Spring AI | Artificial Intelligence

This article was drafted with AI assistance and checked against the sources above. Company claims are reported as claims. Cover image is AI-generated.